ArticleEuropean journal of nuclear medicine and molecular imaging2020
Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism.Scientific reports · 2022Pooled it
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Positron Emission Tomography Imaging in Clinical Trials for Parkinson's Disease: Applications of Metabolic Brain Network Approach.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Parkinson's Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation.Cells · 2025Review
- Multinomial logistic regression algorithm for the classification of patients with parkinsonisms.EJNMMI research · 2025Article
- A candidate loss-of-function variant in SGIP1 causes synaptic dysfunction and recessive parkinsonism.Cell reports. Medicine · 2024Article
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
- Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.NPJ digital medicine · 2024Article
- Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Article
- Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt-Jakob Disease with Concomitant Parkinson's Disease.International journal of molecular sciences · 2023Article
- Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia.Human brain mapping · 2023Article
- Adaptive and pathological connectivity responses in Parkinson's disease brain networks.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Functional brain networks in the evaluation of patients with neurodegenerative disorders.Nature reviews. Neurology · 2023Review
- Parkinson's disease progression: Increasing expression of an invariant common core subnetwork.NeuroImage. Clinical · 2023Article
- Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2022Article
- Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.Frontiers in aging neuroscience · 2022Article
- Single Photon Emission Computed Tomography/Positron Emission Tomography Molecular Imaging for Parkinsonism: A Fast-Developing Field.Annals of neurology · 2021Review
- Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism.European journal of nuclear medicine and molecular imaging · 2021Article
- Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study.Neuroimage. Reports · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDifferentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used
methodsOne hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent
resultsImage-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS.
conclusionThe image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.
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Registered trials
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